The special case of perfect communication cooperative MARL was theoretically analyzed in PAC terms in [1]}, [2]}. More recently, empirical work by [3]}, [4]} suggested necessary properties for effective cooperative MAS exploration, specifically commitment, diversity and adaptivity, and demonstrated that exploiting them improves exploration. Other interesting work focuses on adaptively learning how to perform communication between agents, in addition to learning the desired task [5]}, [6]}, [7]}. It was also recently demonstrated that single-agent meta-learning can be improved by using parallel agents which coordinate their exploration [8]}. Another recent theoretical work [9]} demonstrated finite time convergence of the TD(0) algorithm for the distributed multi-agent case.
